Neuron Models: Simpler Is Better

By Sandra M. Chung

During the summer of 2009, the International Neuroinformatics Coordinating Facility in Stockholm dangled a nearly $10,000 cash prize in front of neuron modelers and challenged them to do better. And they did. The winners of the competition, which was described in the October 16, 2009 issue of Science, produced a neuron model that became more accurate as they stripped away pieces of a much more complex starting model.

“It was amazing for us physicists to see the description become simpler as we tried to make the performance better,” says Shigeru Shinomoto, PhD, a physicist at Kyoto University in Japan who, along with two of his former students, snagged the grand prize.

Modeling the electrical behavior of individual neurons is crucial to understanding how thought and other cognitive functions arise in complex neuronal networks. Current neuron models can predict some neuron behavior, but with limited accuracy and at high computational cost.

The international competition has grown from eight entrants in 2007 to 33 this year and included teams around the world. “We had different people from different backgrounds using methods we would never have thought of,” says Wulfram Gerstner, PhD, a computational neuroscientist at the Ecole Polytechnique Federale in Lausanne, Switzerland who co-authored the Science paper.

Contestants had to predict the precise timing of electrical spikes in individual neurons from different parts of the brain. Since different neurons can respond differently to the same signal, competitors used the first 38 seconds of data from a neuron to adjust their model parameters to better fit that neuron. They used the freshly tuned model to predict spikes in the subsequent 22 seconds of data. Shinomoto’s winning model predicted 59.6 percent and 81.6 percent, respectively, of the spikes from two different neurons.

Electrical activity in a real neuron spikes when its membrane potential passes a set threshold value. Shinomoto’s model neuron has an adapting threshold that increases immediately after a spike and decays exponentially to its initial value. The decay is modulated by two time constants of 10 ms and 200 ms, chosen to reflect the timing of ion currents in the neuron membrane.

The competition will evolve with the field, Gerstner says. Computational neuroscientists will soon draw on an emerging body of molecular knowledge to improve their models, says Erik De Schutter, PhD, a professor of computational neuroscience at the Okinawa Institute of Science and Technology. Advanced molecular techniques should reveal the physical structures and electrical properties of neurons in much greater detail than is currently known. These data may help modelers account for the effects of variations in temperature and chemical conditions and in the physical structures of the neurons.

“Neuron modeling is still a work in progress,” De Schutter says. “It’s much more difficult than we thought.”